WO2014194086A1 - A parallel method for agglomerative clustering of non-stationary data - Google Patents

A parallel method for agglomerative clustering of non-stationary data Download PDF

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Publication number
WO2014194086A1
WO2014194086A1 PCT/US2014/040018 US2014040018W WO2014194086A1 WO 2014194086 A1 WO2014194086 A1 WO 2014194086A1 US 2014040018 W US2014040018 W US 2014040018W WO 2014194086 A1 WO2014194086 A1 WO 2014194086A1
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WO
WIPO (PCT)
Prior art keywords
data points
threads
processors
stream
clusters
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/US2014/040018
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English (en)
French (fr)
Inventor
Isaac David Guedalia
Sarah GLICKFIELD
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Qualcomm Inc
Original Assignee
Qualcomm Inc
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Qualcomm Inc filed Critical Qualcomm Inc
Priority to JP2016516818A priority Critical patent/JP2016530591A/ja
Priority to EP14737357.5A priority patent/EP3005115A1/en
Priority to KR1020157034660A priority patent/KR101793014B1/ko
Priority to CN201480030706.0A priority patent/CN105247487B/zh
Publication of WO2014194086A1 publication Critical patent/WO2014194086A1/en
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/46—Multiprogramming arrangements
    • G06F9/466—Transaction processing
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/46—Multiprogramming arrangements
    • G06F9/54—Interprogram communication
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/04—Inference or reasoning models
    • G06N5/043—Distributed expert systems; Blackboards

Definitions

  • the LAN transceiver 206 comprise another type of local area network, personal area network, (e.g., Bluetooth). Additionally, any other type of wireless networking technologies may be used, for example, Ultra Wide Band, ZigBee, wireless USB etc.
  • wireless access point may be used to refer to LAN- WAPs and/or WAN- WAPs.
  • WAP wireless access point
  • embodiments may include a UE 200 that can exploit signals from a plurality of LAN- WAPs, a plurality of WAN-WAPs, or any combination of the two.
  • the specific type of WAP being utilized by the UE 200 may depend upon the environment of operation.
  • the UE 200 may dynamically select between the various types of WAPs in order to arrive at an accurate position solution.
  • the modules shown in FIG. 2 are illustrated in the example as being contained in the memory 214, it is recognized that in certain implementations such procedures may be provided for or otherwise operatively arranged using other or additional mechanisms.
  • all or part of the wireless-based positioning module 216 and/or the application module 218 may be provided in firmware.
  • the wireless-based positioning module 216 and the application module 218 are illustrated as being separate features, it is recognized, for example, that such procedures may be combined together as one procedure or perhaps with other procedures, or otherwise further divided into a plurality of sub-procedures.
  • the logic configured to process information 310 can include logic configured to receive a stream of data points, logic configured to determine a plurality of cluster centroids, logic configured to divide the plurality of cluster centroids among a plurality of threads and/or processors, logic configured to assign a portion of the stream of data points to each of the plurality of threads and/or processors, and logic configured to combine a plurality of clusters generated by the plurality of threads and/or processors to generate a global universe of clusters.
  • the communication device 300 further optionally includes logic configured to receive local user input 325.
  • the logic configured to receive local user input 325 can include at least a user input device and associated hardware.
  • the user input device can include buttons, a touchscreen display, a keyboard, a camera, an audio input device (e.g., a microphone or a port that can carry audio information such as a microphone jack, etc.), and/or any other device by which information can be received from a user or operator of the communication device 300.
  • the logic configured to receive local user input 325 can include the microphone 252, the keypad 254, the display 256, etc.
  • FIG. 4 illustrates an exemplary listing of representative computer program instructions implementing a k-means algorithm, as illustrated in U.S. Patent No. 6,269,376.
  • the k-means algorithm comprises essentially four steps:
  • the UE can drop data points or reduce the sampling rate (where, for example, the data points are being generated by one or more sensors). Further, if several UEs are coupled over a high-speed data link, whether wired or wireless, the parallel processing can be distributed over the multiple UEs. The UE generating the sensor data can assign it to the other devices and receive the clustering results.
  • FIG. 6 illustrates an exemplary flow for clustering a stream of data points that may be performed by a UE, such as UE 200.
  • the UE receives the stream of data points.
  • the UE may receive the stream of data points from one or more sensors, such as an accelerometer, a gyroscope, a magnetometer, a microphone, and/or the like. If the stream of data points contains too many data points to efficiently process, even with the plurality of threads and/or processors, the UE may drop data points to reduce the number of data points it will have to process, as described above.

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  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Software Systems (AREA)
  • Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Data Mining & Analysis (AREA)
  • Artificial Intelligence (AREA)
  • Evolutionary Computation (AREA)
  • Computing Systems (AREA)
  • Mathematical Physics (AREA)
  • Computational Linguistics (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Medical Informatics (AREA)
  • Mobile Radio Communication Systems (AREA)
  • Telephone Function (AREA)
PCT/US2014/040018 2013-05-30 2014-05-29 A parallel method for agglomerative clustering of non-stationary data Ceased WO2014194086A1 (en)

Priority Applications (4)

Application Number Priority Date Filing Date Title
JP2016516818A JP2016530591A (ja) 2013-05-30 2014-05-29 非定常データの凝集クラスタリングのための並列化方法
EP14737357.5A EP3005115A1 (en) 2013-05-30 2014-05-29 A parallel method for agglomerative clustering of non-stationary data
KR1020157034660A KR101793014B1 (ko) 2013-05-30 2014-05-29 비정상 데이터의 병합식 클러스터링을 위한 병렬 방법
CN201480030706.0A CN105247487B (zh) 2013-05-30 2014-05-29 用于非平稳数据的凝聚群集的并行方法

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US13/906,169 US9411632B2 (en) 2013-05-30 2013-05-30 Parallel method for agglomerative clustering of non-stationary data
US13/906,169 2013-05-30

Publications (1)

Publication Number Publication Date
WO2014194086A1 true WO2014194086A1 (en) 2014-12-04

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PCT/US2014/040018 Ceased WO2014194086A1 (en) 2013-05-30 2014-05-29 A parallel method for agglomerative clustering of non-stationary data

Country Status (6)

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US (1) US9411632B2 (enExample)
EP (1) EP3005115A1 (enExample)
JP (1) JP2016530591A (enExample)
KR (1) KR101793014B1 (enExample)
CN (1) CN105247487B (enExample)
WO (1) WO2014194086A1 (enExample)

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US11461360B2 (en) * 2018-03-30 2022-10-04 AVAST Software s.r.o. Efficiently initializing distributed clustering on large data sets
US11238308B2 (en) * 2018-06-26 2022-02-01 Intel Corporation Entropic clustering of objects
JP7228031B2 (ja) 2018-10-15 2023-02-22 ベンタナ メディカル システムズ， インコーポレイテッド 細胞の分類のためのシステムおよび方法
US11853877B2 (en) 2019-04-02 2023-12-26 International Business Machines Corporation Training transfer-focused models for deep learning
CN110717517A (zh) * 2019-09-06 2020-01-21 中国平安财产保险股份有限公司 智能化多线程聚类方法、装置及计算机可读存储介质
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Also Published As

Publication number Publication date
KR101793014B1 (ko) 2017-11-02
CN105247487A (zh) 2016-01-13
US20140359626A1 (en) 2014-12-04
JP2016530591A (ja) 2016-09-29
KR20160016842A (ko) 2016-02-15
EP3005115A1 (en) 2016-04-13
US9411632B2 (en) 2016-08-09
CN105247487B (zh) 2018-12-28

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